- Email: [email protected]
Value added by MRI for predicting clinical conversion to dementia in a heterogeneous community cohort
S16
IC-P-030
Alzheimer’s Imaging Consortium IC-P: Imaging Posters
MCI DIAGNOSIS VIA MANIFOLD BASED CLASSIFICATION OF FUNCTIONAL BRAIN NETWORKS
Yong Fan1, Jeffrey N. Browndyke2, 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 2Joseph & Kathleen Bryan Alzheimer’s Disease Research Center, Duke University Medical Center, Durham, NC, USA. Contact e-mail: yfan@ nlpr.ia.ac.cn Background: Accumulated evidence from fMRI studies has demonstrated that Alzheimer’s disease (AD) is associated with abnormalities of brain functional networks (FNs) in medial temporal lobe, frontal, temporal, and parietal cortices. Identifying brain FNs affected by mild cognitive impairment (MCI) and optimally utilizing them in diagnosis could potentially improve early detection of AD. This study applied independent component analysis (ICA) to extract brain FNs from fMRI data of MCI and cognitively normal (CN) elderly subjects during their performance of a simple semantic memory task and resting visual fixation. Advanced pattern classification techniques identified the optimal combination of FNs for diagnostic determination. Methods: A manifold based classification algorithm was used to classify fMRI data of 12 MCIs and 12 CNs. A group ICA was used to extract FNs which were encoded by spatial independent components (ICs) and a back-reconstruction technique was used to compute subject specific FNs. The FNs of each individual were used as bases for spanning a linear subspace, referred to as a functional connectivity pattern (FCP), which facilitated a comprehensive characterization of temporal signals of fMRI data. The FCPs of different individuals were analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a k-nearest neighbor classifier, a forward component selection technique was used to select ICs for constructing the most discriminative FCP whose discriminative power was measured using leave-one-out cross validation. Results: We identified a FCP spanned by 7 FNs, including temporal, parietal, and frontal regions, which were most characteristic of MCI. This combined cognitive challenge and resting state FCP correctly distinguished 20 out 24 subjects, cross-validated using leave-one-out method. This result is better than those obtained by the classifiers built on FCPs spanned by either all available FNs (50%) or any individual FN (the best performance: 54.2%). Conclusions: The manifold based classification method has the potential to identify MCI associated FNs, which could serve as surrogate biomarkers for early detection of individuals at risk for AD. Furthermore, the FCP spanned by multiple discriminative FNs has superior diagnostic value compared to either any individual FN or the FCP spanned by all available FNs.
IC-P-031
FORNIX FRACTIONAL ANISOTROPY AND HIPPOCAMPAL VOLUME, ALONE AND IN COMBINATION, PREDICT CONVERSION FROM MCI TO AD
Michelle M. Mielke, Ozioma C. Okonkwo, Susumu Mori, Kenichi Oishi, Michael I. Miller, Can Ceritoglu, Timothy Brown, Marilyn Albert, Constantine G. Lyketsos, Johns Hopkins University, Baltimore, MD, USA. Contact e-mail: [email protected]
Background: Diffusion tensor imaging (DTI) is a method of detecting the integrity of white matter fiber bundles within the brain. The fornix is a predominant outflow tract of the hippocampus. A recent study from our group has reported important differences in fornix integrity between normal controls, amnestic mild cognitive impairment (aMCI), and Alzheimer disease (AD) cases. Fornix integrity is also reduced in presymptomatic PS1 mutation carriers compared to non-carriers. Here we examine whether fornix white matter integrity, as measured via fractional anisotropy (FA), predicts conversion to AD among patients with aMCI, and compare this to the predictive value of hippocampal volume measurement. Methods: Twentythree aMCI patients were enrolled in a longitudinal study and followed for up to 2.5 years. Six participants converted to AD during this time. Baseline DTI data were analyzed with DTIStudio, followed by manual identification of the fornix by skilled operators using a standardized protocol with high reliability. Hippocampus volumes were calculated on binary hippocampus images delineated with a template based segmentation method using the large deformation diffeomorphic metric mapping (LDDMM) algorithm. The predictive value of each neuroimaging measure was examined using ROC analyses and logistic regression. Results: Baseline fornix FA levels discriminated between which MCI cases would convert to AD with lower fornix FA being highly predictive of converting to AD (OR ¼ 80; p ¼ 0.029). Analyses for fornix FA revealed a mean area under the curve (AUC) of 0.946 (95% CI: 0.851-1.00) and a cutoff of fornix FA ¼ 0.45 yielding sensitivity of 83.3% and specificity of 94.1%. This was comparable to the predictive value of both the left (AUC ¼ 0.931; 95% CI: 0.813-1.00) and right (AUC ¼ 0.990; 95% CI: 0.963-1.00) hippocampal volumes, with lower volumes being highly predictive of conversion to AD. A combination of both the right hippocampus and fornix measures resulted in an AUC of 1.00. Conclusions: These findings suggest that fornix FA may be may be a useful, discriminate marker of conversion to AD among aMCI cases, especially when combined with hippocampal volume information. Replication in a larger sample is clearly warranted.
IC-P-032
VALUE ADDED BY MRI FOR PREDICTING CLINICAL CONVERSION TO DEMENTIA IN A HETEROGENEOUS COMMUNITY COHORT
Owen T. Carmichael, Dan Mungas, Laurel Beckett, Danielle Harvey, Sarah T. Farias, Bruce Reed, John Olichney, Joshua Miller, Charles DeCarli, UC Davis, Davis, CA, USA. Contact e-mail: [email protected] Background: MRI measures of brain structure have been shown to predict conversion to dementia in clinic-based samples, but their utility for predicting conversion in combination with other established risk factors in a diverse community setting remains unclear. Therefore we assessed whether MRI-based measures of brain volume (BV), hippocampal volume (HC), and white matter hyperintensity volume (WMH) predicted risk of dementia in a heterogeneous cohort after accounting for demographic variables, psychometrically matched cognitive measures, APOE genotype, and baseline clinical diagnosis. Methods: 289 community-dwelling non-demented participants in the University of California, Davis Alzheimer’s Disease Center Longitudinal cohort received MRI, cognitive testing, and clinical evaluation at baseline, and follow-up clinical evaluations at yearly follow-ups. Participants were highly diverse in terms of ethnicity (Caucasian, Hispanic, and African-American), early life experiences, cardiovascular health, socioeconomic status, and other factors. Sequentiallyconstructed Cox proportional hazard models analyzed the independent contributions of the MRI variables, age, gender, education, ethnicity, APOE genotype, baseline clinical diagnosis (including MCI subtypes), and psychometrically-matched measures of semantic memory, episodic memory, executive function, and visuospatial function for increasing or decreasing risk of dementia diagnosis on follow-up. Results: BV, WMH, semantic memory, APOE genotype, and baseline clinical diagnosis were each independently associated with increased risk of dementia after accounting for other demographic and cognitive variables and HC.
Alzheimer’s Imaging Consortium IC-P: Imaging Posters
S17
Conclusions: Even after controlling for several other established risk factors for dementia, MRI provides independent power to predict dementia in a heterogeneous community cohort. The utility of MRI for predicting dementia may generalize well to a diverse community setting. IC-P-033
CSF ABETA AND TAU, HIPPOCAMPAL ATROPHY AND LATERAL VENTRICLE ENLARGEMENT IN PARKINSON’S DISEASE WITH AND WITHOUT MILD COGNITIVE IMPAIRMENT
Mona K. Beyer1, Kristy S. Hwang2, Sona Babakchanian2, Paul M. Thompson3, Jeffrey L. Cummings2, Ezra Mulugeta1, Jan P. Larsen1, Kolbjorn S. Bronnick1, Dag Aarsland4, Guido Alves1, Liana G. Apostolova2, 1 Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway; 2Mary S Easton Center for Alzheimer’s Disease Research, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; 3 Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA; 4Psychiatric Clinic, Stavanger University Hospital, Stavanger, Norway. Contact e-mail: [email protected] Background: ParkWest is a large multicenter study of newly diagnosed drug-naı¨ve Parkinson’s disease (PD) subjects. Baseline imaging data analyses showed that cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) have significant hippocampal atrophy and ventricular enlargement. Whether these changes correlate with known cerebrospinal fluid (CSF) neurodegenerative biomarkers is not known. Methods: We analyzed baseline T1-weighted MR data of all ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We used Abeta triplex assay (Human Ab peptide Ultra-Sensitive, 4G8 antibody) and ELISA InnotestÒ hTAUAg (t-tau) and phospho-tau(181P) (p-tau). The hippocampi and lateral ventricles were segmented with two novel automated segmentation techniques and further analyzed with the radial distance approach. We applied linear regression models to study the associations between CSF biomarkers and hippocampal and ventricular radial distance while adjusting for center. For multiple comparison correction we used permutations with threshold p < 0.01. Results: T-tau showed a significant negative association with left hippocampal radial distance in the pooled sample (pcorrected ¼ 0.05). Abeta38, Abeta40, Abeta42 and p-tau showed no significant associations with hippocampal radial distance. In the pooled ventricular analysis Abeta38 and Abeta42 showed significant negative associations with the occipital (Abeta38 left pcorrected ¼ 0.02, right pcorrected ¼ 0.03; Abeta42 left pcorrected ¼ 0.04, right pcorrected ¼ 0.03) and frontal horns (Abeta38 left pcorrected ¼ 0.007, right pcorrected ¼ 0.01; Abeta42 left pcorrected ¼ 0.01, right pcorrected ¼ 0.007). Abeta40 showed a negative association restricted to the right occipital horn in PDMCI (pcorrected ¼ 0.02). Abeta42 showed a negative association restricted to the occipital horns in PDMCI (left pcorrected ¼ 0.02, right pcorrected ¼ 0.03) and the frontal horns in PDCN (left pcorrected ¼ 0.07, right pcorrected ¼ 0.02). T-tau and p-tau showed no significant associations with the lateral ventricles. Conclusions: Our results suggest that CSF biomarkers traditionally associated with Alzheimer’s disease (AD) show selective associations with structural brain changes in PD. Increased t-tau levels correlate with hippocampal atrophy while CSF levels of several Abeta species show a correlation with ventricular enlargement. The changes observed in AD are severe tau-associated hippocampal degeneration and amyloid-associated cortical and global brain atrophy. Here we find similar evidence in PD. Our findings suggest that some pathophysiologic events may be common to both disorders.
IC-P-034
COGNITIVE EVENT-RELATED POTENTIALS AS INDICATORS OF DONEPEZIL TREATMENT EFFECTS IN MILD ALZHEIMER’S DISEASE
Zoe¨ Tieges1, Kerry W. Kilborn1, Jessica Price2, Bernard A. Conway2, Alan Hughes3, Gillian McLean4, 1Glasgow University, Glasgow, United Kingdom; 2Strathclyde University, Glasgow, United Kingdom; 3Greater Glasgow NHS, Glasgow, United Kingdom; 4Forth Valley NHS, Falkirk, United Kingdom. Contact e-mail: [email protected] Background: In previous research we identified ERP and behavioural features associated with memory that produced good discrimination between mild AD and controls. Two prominent markers include a difference wave around 600ms, and memory performance (d’ measures). In the current study, we explore whether these markers of brain and cognitive function are sensitive to treatment effects of donepezil. Methods: We examined a range of ERP components and behavioral data based on a crossmodal episodic memory task designed to elicit activity in the hippocampus. The task entails an old/ new decision to simple visual images coupled with auditory words. For example, the image of a train is presented with the word ‘‘tunnel.’’ Some pairs are presented a second or third time, at varying intervals. Dense array (128 channel) EEG is acquired continuously during testing. Patients newly diagnosed with probable AD participated in four successive test sessions: pre-treatment Session 1, and post-treatment Session 2 (1 week), Session 3 (4 weeks) and Session 4 (12 weeks). All patients received donepezil. We report here the findings for 8 patients who have completed all test sessions in the ongoing study. Results: We observed significant effects between pre-treatment and posttreatment conditions, as measured by both behaviorial (d’) and ERP variables. T-tests with the d’ measure show significant differences between Session 1 and 3 (t ¼ -2.45, p < 0.045), and between Session 3 and 4 (t ¼ -2.50, p < .042). These differences indicate an improvement in memory task performance is associated with treatment. We also observed memory-related ERP effects that vary according to treatment condition. In the time interval 520580ms post-stimulus, in healthy controls we observe a left frontal negativity that is coupled with a right parietal positivity. Pre-treatment, this effect is greatly attenuated in AD patients. However, by Session 4 (12 weeks), we observe apparent recovery of this functional ERP pattern (Memory x Session, F ¼ 3.45, p < 0.009; Session 1 vs Session 4, F ¼ 12.88, p < .013). Conclusions: The improvement in memory performance, together with the recovery of brain function, indicate that cognitive ERP methods are sensitive to treatment affects of donepezil in AD. IC-P-035
EFFECTS OF MEMANTINE ON MAINTAINING WHITE MATTER INTEGRITY IN ALZHEIMER’S DISEASE PATIENTS WITH AGITATION
Xiao Wang1, Huali Wang1, Jing Liao2, Huishu Yuan2, Xin Yu1, 1Dementia Care & Research Center, Peking University Institute of Mental Health; Key Laboratory for Mental Health, Ministry of Health (Peking University), BeiJing, China; 2Department of Radiology, Peking University Third Hospital, Beijing, China. Contact e-mail: [email protected]
IC-P-030
Alzheimer’s Imaging Consortium IC-P: Imaging Posters
MCI DIAGNOSIS VIA MANIFOLD BASED CLASSIFICATION OF FUNCTIONAL BRAIN NETWORKS
Yong Fan1, Jeffrey N. Browndyke2, 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 2Joseph & Kathleen Bryan Alzheimer’s Disease Research Center, Duke University Medical Center, Durham, NC, USA. Contact e-mail: yfan@ nlpr.ia.ac.cn Background: Accumulated evidence from fMRI studies has demonstrated that Alzheimer’s disease (AD) is associated with abnormalities of brain functional networks (FNs) in medial temporal lobe, frontal, temporal, and parietal cortices. Identifying brain FNs affected by mild cognitive impairment (MCI) and optimally utilizing them in diagnosis could potentially improve early detection of AD. This study applied independent component analysis (ICA) to extract brain FNs from fMRI data of MCI and cognitively normal (CN) elderly subjects during their performance of a simple semantic memory task and resting visual fixation. Advanced pattern classification techniques identified the optimal combination of FNs for diagnostic determination. Methods: A manifold based classification algorithm was used to classify fMRI data of 12 MCIs and 12 CNs. A group ICA was used to extract FNs which were encoded by spatial independent components (ICs) and a back-reconstruction technique was used to compute subject specific FNs. The FNs of each individual were used as bases for spanning a linear subspace, referred to as a functional connectivity pattern (FCP), which facilitated a comprehensive characterization of temporal signals of fMRI data. The FCPs of different individuals were analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a k-nearest neighbor classifier, a forward component selection technique was used to select ICs for constructing the most discriminative FCP whose discriminative power was measured using leave-one-out cross validation. Results: We identified a FCP spanned by 7 FNs, including temporal, parietal, and frontal regions, which were most characteristic of MCI. This combined cognitive challenge and resting state FCP correctly distinguished 20 out 24 subjects, cross-validated using leave-one-out method. This result is better than those obtained by the classifiers built on FCPs spanned by either all available FNs (50%) or any individual FN (the best performance: 54.2%). Conclusions: The manifold based classification method has the potential to identify MCI associated FNs, which could serve as surrogate biomarkers for early detection of individuals at risk for AD. Furthermore, the FCP spanned by multiple discriminative FNs has superior diagnostic value compared to either any individual FN or the FCP spanned by all available FNs.
IC-P-031
FORNIX FRACTIONAL ANISOTROPY AND HIPPOCAMPAL VOLUME, ALONE AND IN COMBINATION, PREDICT CONVERSION FROM MCI TO AD
Michelle M. Mielke, Ozioma C. Okonkwo, Susumu Mori, Kenichi Oishi, Michael I. Miller, Can Ceritoglu, Timothy Brown, Marilyn Albert, Constantine G. Lyketsos, Johns Hopkins University, Baltimore, MD, USA. Contact e-mail: [email protected]
Background: Diffusion tensor imaging (DTI) is a method of detecting the integrity of white matter fiber bundles within the brain. The fornix is a predominant outflow tract of the hippocampus. A recent study from our group has reported important differences in fornix integrity between normal controls, amnestic mild cognitive impairment (aMCI), and Alzheimer disease (AD) cases. Fornix integrity is also reduced in presymptomatic PS1 mutation carriers compared to non-carriers. Here we examine whether fornix white matter integrity, as measured via fractional anisotropy (FA), predicts conversion to AD among patients with aMCI, and compare this to the predictive value of hippocampal volume measurement. Methods: Twentythree aMCI patients were enrolled in a longitudinal study and followed for up to 2.5 years. Six participants converted to AD during this time. Baseline DTI data were analyzed with DTIStudio, followed by manual identification of the fornix by skilled operators using a standardized protocol with high reliability. Hippocampus volumes were calculated on binary hippocampus images delineated with a template based segmentation method using the large deformation diffeomorphic metric mapping (LDDMM) algorithm. The predictive value of each neuroimaging measure was examined using ROC analyses and logistic regression. Results: Baseline fornix FA levels discriminated between which MCI cases would convert to AD with lower fornix FA being highly predictive of converting to AD (OR ¼ 80; p ¼ 0.029). Analyses for fornix FA revealed a mean area under the curve (AUC) of 0.946 (95% CI: 0.851-1.00) and a cutoff of fornix FA ¼ 0.45 yielding sensitivity of 83.3% and specificity of 94.1%. This was comparable to the predictive value of both the left (AUC ¼ 0.931; 95% CI: 0.813-1.00) and right (AUC ¼ 0.990; 95% CI: 0.963-1.00) hippocampal volumes, with lower volumes being highly predictive of conversion to AD. A combination of both the right hippocampus and fornix measures resulted in an AUC of 1.00. Conclusions: These findings suggest that fornix FA may be may be a useful, discriminate marker of conversion to AD among aMCI cases, especially when combined with hippocampal volume information. Replication in a larger sample is clearly warranted.
IC-P-032
VALUE ADDED BY MRI FOR PREDICTING CLINICAL CONVERSION TO DEMENTIA IN A HETEROGENEOUS COMMUNITY COHORT
Owen T. Carmichael, Dan Mungas, Laurel Beckett, Danielle Harvey, Sarah T. Farias, Bruce Reed, John Olichney, Joshua Miller, Charles DeCarli, UC Davis, Davis, CA, USA. Contact e-mail: [email protected] Background: MRI measures of brain structure have been shown to predict conversion to dementia in clinic-based samples, but their utility for predicting conversion in combination with other established risk factors in a diverse community setting remains unclear. Therefore we assessed whether MRI-based measures of brain volume (BV), hippocampal volume (HC), and white matter hyperintensity volume (WMH) predicted risk of dementia in a heterogeneous cohort after accounting for demographic variables, psychometrically matched cognitive measures, APOE genotype, and baseline clinical diagnosis. Methods: 289 community-dwelling non-demented participants in the University of California, Davis Alzheimer’s Disease Center Longitudinal cohort received MRI, cognitive testing, and clinical evaluation at baseline, and follow-up clinical evaluations at yearly follow-ups. Participants were highly diverse in terms of ethnicity (Caucasian, Hispanic, and African-American), early life experiences, cardiovascular health, socioeconomic status, and other factors. Sequentiallyconstructed Cox proportional hazard models analyzed the independent contributions of the MRI variables, age, gender, education, ethnicity, APOE genotype, baseline clinical diagnosis (including MCI subtypes), and psychometrically-matched measures of semantic memory, episodic memory, executive function, and visuospatial function for increasing or decreasing risk of dementia diagnosis on follow-up. Results: BV, WMH, semantic memory, APOE genotype, and baseline clinical diagnosis were each independently associated with increased risk of dementia after accounting for other demographic and cognitive variables and HC.
Alzheimer’s Imaging Consortium IC-P: Imaging Posters
S17
Conclusions: Even after controlling for several other established risk factors for dementia, MRI provides independent power to predict dementia in a heterogeneous community cohort. The utility of MRI for predicting dementia may generalize well to a diverse community setting. IC-P-033
CSF ABETA AND TAU, HIPPOCAMPAL ATROPHY AND LATERAL VENTRICLE ENLARGEMENT IN PARKINSON’S DISEASE WITH AND WITHOUT MILD COGNITIVE IMPAIRMENT
Mona K. Beyer1, Kristy S. Hwang2, Sona Babakchanian2, Paul M. Thompson3, Jeffrey L. Cummings2, Ezra Mulugeta1, Jan P. Larsen1, Kolbjorn S. Bronnick1, Dag Aarsland4, Guido Alves1, Liana G. Apostolova2, 1 Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway; 2Mary S Easton Center for Alzheimer’s Disease Research, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; 3 Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA; 4Psychiatric Clinic, Stavanger University Hospital, Stavanger, Norway. Contact e-mail: [email protected] Background: ParkWest is a large multicenter study of newly diagnosed drug-naı¨ve Parkinson’s disease (PD) subjects. Baseline imaging data analyses showed that cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) have significant hippocampal atrophy and ventricular enlargement. Whether these changes correlate with known cerebrospinal fluid (CSF) neurodegenerative biomarkers is not known. Methods: We analyzed baseline T1-weighted MR data of all ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We used Abeta triplex assay (Human Ab peptide Ultra-Sensitive, 4G8 antibody) and ELISA InnotestÒ hTAUAg (t-tau) and phospho-tau(181P) (p-tau). The hippocampi and lateral ventricles were segmented with two novel automated segmentation techniques and further analyzed with the radial distance approach. We applied linear regression models to study the associations between CSF biomarkers and hippocampal and ventricular radial distance while adjusting for center. For multiple comparison correction we used permutations with threshold p < 0.01. Results: T-tau showed a significant negative association with left hippocampal radial distance in the pooled sample (pcorrected ¼ 0.05). Abeta38, Abeta40, Abeta42 and p-tau showed no significant associations with hippocampal radial distance. In the pooled ventricular analysis Abeta38 and Abeta42 showed significant negative associations with the occipital (Abeta38 left pcorrected ¼ 0.02, right pcorrected ¼ 0.03; Abeta42 left pcorrected ¼ 0.04, right pcorrected ¼ 0.03) and frontal horns (Abeta38 left pcorrected ¼ 0.007, right pcorrected ¼ 0.01; Abeta42 left pcorrected ¼ 0.01, right pcorrected ¼ 0.007). Abeta40 showed a negative association restricted to the right occipital horn in PDMCI (pcorrected ¼ 0.02). Abeta42 showed a negative association restricted to the occipital horns in PDMCI (left pcorrected ¼ 0.02, right pcorrected ¼ 0.03) and the frontal horns in PDCN (left pcorrected ¼ 0.07, right pcorrected ¼ 0.02). T-tau and p-tau showed no significant associations with the lateral ventricles. Conclusions: Our results suggest that CSF biomarkers traditionally associated with Alzheimer’s disease (AD) show selective associations with structural brain changes in PD. Increased t-tau levels correlate with hippocampal atrophy while CSF levels of several Abeta species show a correlation with ventricular enlargement. The changes observed in AD are severe tau-associated hippocampal degeneration and amyloid-associated cortical and global brain atrophy. Here we find similar evidence in PD. Our findings suggest that some pathophysiologic events may be common to both disorders.
IC-P-034
COGNITIVE EVENT-RELATED POTENTIALS AS INDICATORS OF DONEPEZIL TREATMENT EFFECTS IN MILD ALZHEIMER’S DISEASE
Zoe¨ Tieges1, Kerry W. Kilborn1, Jessica Price2, Bernard A. Conway2, Alan Hughes3, Gillian McLean4, 1Glasgow University, Glasgow, United Kingdom; 2Strathclyde University, Glasgow, United Kingdom; 3Greater Glasgow NHS, Glasgow, United Kingdom; 4Forth Valley NHS, Falkirk, United Kingdom. Contact e-mail: [email protected] Background: In previous research we identified ERP and behavioural features associated with memory that produced good discrimination between mild AD and controls. Two prominent markers include a difference wave around 600ms, and memory performance (d’ measures). In the current study, we explore whether these markers of brain and cognitive function are sensitive to treatment effects of donepezil. Methods: We examined a range of ERP components and behavioral data based on a crossmodal episodic memory task designed to elicit activity in the hippocampus. The task entails an old/ new decision to simple visual images coupled with auditory words. For example, the image of a train is presented with the word ‘‘tunnel.’’ Some pairs are presented a second or third time, at varying intervals. Dense array (128 channel) EEG is acquired continuously during testing. Patients newly diagnosed with probable AD participated in four successive test sessions: pre-treatment Session 1, and post-treatment Session 2 (1 week), Session 3 (4 weeks) and Session 4 (12 weeks). All patients received donepezil. We report here the findings for 8 patients who have completed all test sessions in the ongoing study. Results: We observed significant effects between pre-treatment and posttreatment conditions, as measured by both behaviorial (d’) and ERP variables. T-tests with the d’ measure show significant differences between Session 1 and 3 (t ¼ -2.45, p < 0.045), and between Session 3 and 4 (t ¼ -2.50, p < .042). These differences indicate an improvement in memory task performance is associated with treatment. We also observed memory-related ERP effects that vary according to treatment condition. In the time interval 520580ms post-stimulus, in healthy controls we observe a left frontal negativity that is coupled with a right parietal positivity. Pre-treatment, this effect is greatly attenuated in AD patients. However, by Session 4 (12 weeks), we observe apparent recovery of this functional ERP pattern (Memory x Session, F ¼ 3.45, p < 0.009; Session 1 vs Session 4, F ¼ 12.88, p < .013). Conclusions: The improvement in memory performance, together with the recovery of brain function, indicate that cognitive ERP methods are sensitive to treatment affects of donepezil in AD. IC-P-035
EFFECTS OF MEMANTINE ON MAINTAINING WHITE MATTER INTEGRITY IN ALZHEIMER’S DISEASE PATIENTS WITH AGITATION
Xiao Wang1, Huali Wang1, Jing Liao2, Huishu Yuan2, Xin Yu1, 1Dementia Care & Research Center, Peking University Institute of Mental Health; Key Laboratory for Mental Health, Ministry of Health (Peking University), BeiJing, China; 2Department of Radiology, Peking University Third Hospital, Beijing, China. Contact e-mail: [email protected]